Goat data analysis
Tess Azevedo
17 juin 2022
TMM normalization
goat<-read.table("../data/goat.csv")
goatt<-t(goat)library("corrplot")## corrplot 0.90 loadedlibrary("FactoMineR")
library("factoextra")## Loading required package: ggplot2## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBalibrary("reshape2")
library("ggplot2")
library("Hmisc")## Loading required package: lattice## Loading required package: survival## Loading required package: Formula##
## Attaching package: 'Hmisc'## The following objects are masked from 'package:base':
##
## format.pval, unitsData Analysis
summary(goat)## cd4_goat1 cd4_goat2 cd4_goat3
## Min. : 0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
## Median : 0.672 Median : 0.797 Median : 0.857
## Mean : 33.777 Mean : 35.090 Mean : 32.679
## 3rd Qu.: 20.914 3rd Qu.: 20.940 3rd Qu.: 19.929
## Max. :12587.386 Max. :14665.324 Max. :12121.344
## cd4_goat4 cd8_goat1 cd8_goat2 cd8_goat3
## Min. : 0.000 Min. : 0.000 Min. : 0.00 Min. : 0.000
## 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.00 1st Qu.: 0.000
## Median : 0.777 Median : 0.787 Median : 0.85 Median : 0.898
## Mean : 32.408 Mean : 34.065 Mean : 34.76 Mean : 34.289
## 3rd Qu.: 20.428 3rd Qu.: 21.132 3rd Qu.: 21.23 3rd Qu.: 20.485
## Max. :11393.197 Max. :12622.673 Max. :13835.64 Max. :12630.834
## cd8_goat4 cerebellum_goat1 cerebellum_goat2
## Min. : 0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 0.000 1st Qu.: 0.030 1st Qu.: 0.033
## Median : 0.839 Median : 2.713 Median : 2.580
## Mean : 33.944 Mean : 31.835 Mean : 30.986
## 3rd Qu.: 20.865 3rd Qu.: 22.960 3rd Qu.: 22.700
## Max. :10231.537 Max. :20765.002 Max. :18409.420
## cerebellum_goat3 cerebellum_goat4 ileum_goat4
## Min. : 0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 0.036 1st Qu.: 0.037 1st Qu.: 0.029
## Median : 2.759 Median : 2.897 Median : 2.765
## Mean : 31.843 Mean : 31.575 Mean : 35.768
## 3rd Qu.: 23.463 3rd Qu.: 23.958 3rd Qu.: 24.055
## Max. :19318.600 Max. :18025.833 Max. :13249.520
## liver_goat1 liver_goat2 liver_goat3 liver_goat4
## Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 0.00
## 1st Qu.: 0.02 1st Qu.: 0.02 1st Qu.: 0.01 1st Qu.: 0.02
## Median : 2.62 Median : 2.76 Median : 2.21 Median : 2.83
## Mean : 72.29 Mean : 74.50 Mean : 64.65 Mean : 65.60
## 3rd Qu.: 25.68 3rd Qu.: 25.19 3rd Qu.: 25.36 3rd Qu.: 25.87
## Max. :129798.50 Max. :165934.97 Max. :97641.67 Max. :67000.18
## lung_goat1 lung_goat2 lung_goat3 lung_goat4
## Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 0.040 1st Qu.: 0.036 1st Qu.: 0.040 1st Qu.: 0.033
## Median : 3.763 Median : 3.487 Median : 3.756 Median : 3.591
## Mean : 33.650 Mean : 34.410 Mean : 33.577 Mean : 32.588
## 3rd Qu.: 25.210 3rd Qu.: 25.019 3rd Qu.: 24.903 3rd Qu.: 24.499
## Max. :9130.728 Max. :8163.974 Max. :9260.541 Max. :8023.443
## muscle_goat1 muscle_goat2 muscle_goat3 muscle_goat4
## Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 0.00
## 1st Qu.: 0.00 1st Qu.: 0.00 1st Qu.: 0.00 1st Qu.: 0.00
## Median : 1.63 Median : 1.32 Median : 1.09 Median : 1.41
## Mean : 92.10 Mean : 82.52 Mean : 88.09 Mean : 75.06
## 3rd Qu.: 21.96 3rd Qu.: 21.36 3rd Qu.: 21.09 3rd Qu.: 21.26
## Max. :107851.12 Max. :95552.58 Max. :168102.68 Max. :69647.12
## testis_goat1 testis_goat2
## Min. : 0.000 Min. : 0.000
## 1st Qu.: 0.213 1st Qu.: 0.217
## Median : 3.963 Median : 3.917
## Mean : 36.527 Mean : 33.845
## 3rd Qu.: 27.942 3rd Qu.: 26.466
## Max. :3268.795 Max. :3054.008boxplot(goat)
boxplot(goat,outline=FALSE) #outliers are not drawn
#percentage of 0
percent_zero<-(colSums(goat==0)/nrow(goat))*100
plot(percent_zero,col="purple",main="Percentage of 0 per tissue and subject",type="b")
#median
list_median<-apply(goat,2,median)
plot(list_median,col="blue",main="Median per tissue and subject",type="b",xlab="Tissues")
goat50=goatt[,1:50]
lapply(1:ncol(goat50), function(i) hist(goat50[,i],xlab="Expression",main=paste("Histogram of" ,colnames(goat50)[i])))
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## $counts
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## $density
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## [8] 0.07407407
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## $xname
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## $equidist
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## [[29]]
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## $xname
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## attr(,"class")
## [1] "histogram"tissue_split <- strsplit(colnames(goat),"_")
tissue <- sapply(tissue_split,function(x){return (x[1])})goat2 <- goat
colnames(goat2) <- tissue
goat2 <- melt(as.matrix(goat2))
colnames(goat2) <- c("Gene","Tissue","Expression")ggplot(data=goat2,aes(x=Tissue,y=Expression,color=Tissue))+geom_boxplot()+ labs(title="Goat tissues boxplots")+theme(legend.position="none")
eps=1e-6
ggplot(data=goat2,aes(x=Tissue,y=log(Expression+eps),color=Tissue))+geom_boxplot()+ labs(title="Goat tissues boxplots")+theme(legend.position="none")
Principal Component Analysis
goat_acp<-PCA(goatt,graph=FALSE) #the individuals are the tissues and the variables are the genes fviz_pca_ind (goat_acp, col.ind = "cos2",
gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
repel = TRUE # Évite le chevauchement de texte
)## Warning: ggrepel: 15 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
fviz_eig(goat_acp, addlabels = TRUE) #to visualize how many components are necessary 
corrplot(cor(goat),method="ellipse")
corrplot(cor(goatt[,1:30]),method="ellipse")
corrplot(cor(goatt[,1:50]),method="ellipse")
corrplot(cor(goatt[,1000:1030]),method="ellipse")
Loess Normalization
goat_loess<-read.table("../data/goat_loess.csv")
goat_loesst<-t(goat_loess)Data Analysis
summary(goat_loess)## cd4_goat1 cd4_goat2 cd4_goat3 cd4_goat4
## Min. : 0.00 Min. : 0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 0.00 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
## Median : 1.42 Median : 1.579 Median : 1.697 Median : 1.559
## Mean : 29.01 Mean : 29.597 Mean : 28.741 Mean : 28.642
## 3rd Qu.: 23.48 3rd Qu.: 23.147 3rd Qu.: 21.917 3rd Qu.: 22.467
## Max. :6702.30 Max. :6607.418 Max. :6546.953 Max. :7038.558
## cd8_goat1 cd8_goat2 cd8_goat3 cd8_goat4
## Min. : 0.00 Min. : 0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 0.00 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
## Median : 1.45 Median : 1.516 Median : 1.624 Median : 1.573
## Mean : 28.52 Mean : 28.861 Mean : 29.751 Mean : 29.785
## 3rd Qu.: 22.75 3rd Qu.: 22.715 3rd Qu.: 22.511 3rd Qu.: 22.920
## Max. :6067.88 Max. :6179.076 Max. :6404.083 Max. :6400.909
## cerebellum_goat1 cerebellum_goat2 cerebellum_goat3 cerebellum_goat4
## Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 0.00
## 1st Qu.: 0.03 1st Qu.: 0.03 1st Qu.: 0.03 1st Qu.: 0.03
## Median : 1.93 Median : 1.92 Median : 1.92 Median : 1.93
## Mean : 43.37 Mean : 44.64 Mean : 43.92 Mean : 40.39
## 3rd Qu.: 22.71 3rd Qu.: 22.93 3rd Qu.: 22.84 3rd Qu.: 22.65
## Max. :80599.58 Max. :89199.93 Max. :83855.13 Max. :62475.01
## ileum_goat4 liver_goat1 liver_goat2 liver_goat3
## Min. : 0.000 Min. : 0.00 Min. : 0.00 Min. : 0.00
## 1st Qu.: 0.032 1st Qu.: 0.02 1st Qu.: 0.02 1st Qu.: 0.01
## Median : 2.358 Median : 2.14 Median : 2.23 Median : 2.09
## Mean : 36.352 Mean : 61.20 Mean : 64.70 Mean : 58.74
## 3rd Qu.: 23.983 3rd Qu.: 23.68 3rd Qu.: 23.44 3rd Qu.: 23.99
## Max. :15633.909 Max. :78155.61 Max. :100416.48 Max. :74426.64
## liver_goat4 lung_goat1 lung_goat2 lung_goat3
## Min. : 0.00 Min. : 0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 0.02 1st Qu.: 0.030 1st Qu.: 0.025 1st Qu.: 0.028
## Median : 2.20 Median : 2.189 Median : 2.133 Median : 2.279
## Mean : 60.04 Mean : 29.228 Mean : 30.161 Mean : 29.716
## 3rd Qu.: 23.29 3rd Qu.: 21.674 3rd Qu.: 21.617 3rd Qu.: 21.831
## Max. :56324.83 Max. :7720.992 Max. :7011.222 Max. :7485.892
## lung_goat4 muscle_goat1 muscle_goat2 muscle_goat3
## Min. : 0.000 Min. : 0.00 Min. : 0.00 Min. : 0.00
## 1st Qu.: 0.029 1st Qu.: 0.00 1st Qu.: 0.00 1st Qu.: 0.00
## Median : 2.275 Median : 1.93 Median : 1.79 Median : 1.64
## Mean : 30.360 Mean : 63.79 Mean : 63.24 Mean : 66.46
## 3rd Qu.: 22.030 3rd Qu.: 23.08 3rd Qu.: 23.79 3rd Qu.: 24.72
## Max. :8257.394 Max. :38837.86 Max. :40558.02 Max. :72589.46
## muscle_goat4 testis_goat1 testis_goat2
## Min. : 0.000 Min. : 0.00 Min. : 0.00
## 1st Qu.: 0.000 1st Qu.: 0.04 1st Qu.: 0.05
## Median : 1.835 Median : 2.37 Median : 2.40
## Mean : 57.033 Mean : 63.51 Mean : 64.39
## 3rd Qu.: 23.789 3rd Qu.: 27.00 3rd Qu.: 26.67
## Max. :28664.987 Max. :65135.92 Max. :77294.82boxplot(goat_loess)
boxplot(goat_loess,outline=FALSE) #outliers are not drawn
#percentage of 0
percent_zero<-(colSums(goat_loess==0)/nrow(goat_loess))*100
plot(percent_zero,col="purple",main="Percentage of 0 per tissue and subject",type="b")
#median
list_median<-apply(goat_loess,2,median)
plot(list_median,col="blue",main="Median per tissue and subject",type="b",xlab="Tissues")
tissue_split <- strsplit(colnames(goat),"_")
tissue <- sapply(tissue_split,function(x){return (x[1])})goat_loess2 <- goat_loess
colnames(goat_loess2) <- tissue
goat_loess2 <- melt(as.matrix(goat_loess2))
colnames(goat_loess2) <- c("Gene","Tissue","Expression")ggplot(data=goat_loess2,aes(x=Tissue,y=Expression,color=Tissue))+geom_boxplot()+labs(title="goat_loess tissues boxplots")+ theme(legend.position="none")
When choosing to fill in colors the outliers on red, all points are colored in red less the horizontal bar on level 0.
eps=1e-6
ggplot(data=goat_loess2,aes(x=Tissue,y=log(Expression+eps),color=Tissue))+geom_boxplot()+labs(title="goat_loess tissues boxplots")+ theme(legend.position="none")
Principal Component Analysis
goat_loess_acp<-PCA(goat_loesst,graph=FALSE) #the individuals are the tissues and the variables are the genes fviz_pca_ind (goat_loess_acp, col.ind = "cos2",
gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
repel = TRUE # Évite le chevauchement de texte
)## Warning: ggrepel: 14 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
fviz_eig(goat_loess_acp, addlabels = TRUE) #to visualize how many components are necessary 
corrplot(cor(goat),method="ellipse")
corrplot(cor(goat_loess),method="ellipse")
TPM NORMALIZATION DATA ANALYSIS
goat_tpm<-read.table("../data/goat_tpm.csv")
#transformations needed to have the same matrix structure that other goats data
#lines
rownames(goat_tpm)<-as.character(goat_tpm[,1])
#colomns
colnames(goat_tpm)<-as.character(goat_tpm[1,])
goat_tpm<-goat_tpm[c(-1),c(-1)]
goat_tpm<-type.convert(goat_tpm) #by defaul numbers were as characters instead of numeric
goat_tpmt<-t(goat_tpm)boxplot(goat_tpm)
boxplot(goat_tpm,outline=FALSE) #outliers are not drawn
#percentage of 0
percent_zero<-(colSums(goat_tpm==0)/nrow(goat_tpm))*100
plot(percent_zero,col="purple",main="Percentage of 0 per tissue and subject",type="b")
#median
list_median<-apply(goat_tpm,2,median)
plot(list_median,col="blue",main="Median per tissue and subject",type="b",xlab="Tissues")
tissue_split <- strsplit(colnames(goat_tpm),"_")
tissue <- sapply(tissue_split,function(x){return (x[1])})goat_tpm2 <- goat_tpm
colnames(goat_tpm2) <- tissue
goat_tpm2 <- melt(as.matrix(goat_tpm2))
colnames(goat_tpm2) <- c("Gene","Tissue","Expression")ggplot(data=goat_tpm2,aes(x=Tissue,y=Expression,color=Tissue))+geom_boxplot()+labs(title="goat_tpm tissues boxplots")+ theme(legend.position="none")
eps=1e-6
ggplot(data=goat_tpm2,aes(x=Tissue,y=log(Expression+eps),color=Tissue))+geom_boxplot()+labs(title="goat_tpm tissues boxplots")+ theme(legend.position="none")
Principal Component Analysis and comparison with other normalizatons
library("FactoMineR")
library("factoextra")
goat_tpm_acp<-PCA(goat_tpmt,graph=FALSE) #the individuals are the tissues and the variables are the genes fviz_pca_ind (goat_tpm_acp, col.ind = "cos2",
gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
repel = TRUE # Évite le chevauchement de texte
)## Warning: ggrepel: 16 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
fviz_eig(goat_tpm_acp, addlabels = TRUE) #to visualize how many components are necessary 
corrplot(cor(goat),method="ellipse")
corrplot(cor(goat_loess),method="ellipse")
corrplot(cor(goat_tpm),method="ellipse")
A significant difference is testis that is correlated to several tissues
and mostly the cerebellum. LIver and muscle not significantly correlated
to others in all three normalizations.
par(mfrow=c(1,3))
ind_goat<-get_pca_ind(goat_acp)
corrplot(ind_goat$cos2, is.corr=FALSE,title = "TMM normalization",mar=c(0,0,2,0))
ind_goat_loess<-get_pca_ind(goat_loess_acp)
corrplot(ind_goat_loess$cos2, is.corr=FALSE,title = "Loess normalization",mar=c(0,0,2,0))
ind_goat_tpm<-get_pca_ind(goat_tpm_acp)
corrplot(ind_goat_tpm$cos2, is.corr=FALSE,title = "TPM normalization",mar=c(0,0,2,0))
For all normalizations testis represent around 80% or more dimension 1. For the others, we have essenciall cerebellum ans lung that contribute fort the other dimensions.
par(mfrow=c(2,2))
eps=1e-6
ggplot(data=goat2,aes(x=Tissue,y=log(Expression+eps),color=Tissue))+geom_boxplot()+ labs(title="Goat tissues boxplots")+theme(legend.position="none")
ggplot(data=goat_loess2,aes(x=Tissue,y=log(Expression+eps),color=Tissue))+geom_boxplot()+ labs(title="Goat_loess tissues boxplots")+theme(legend.position="none")
ggplot(data=goat_tpm2,aes(x=Tissue,y=log(Expression+eps),color=Tissue))+geom_boxplot()+ labs(title="Goat_tpm tissues boxplots")+theme(legend.position="none")
par(mfrow=c(1,3))
fviz_eig(goat_acp, addlabels = TRUE) #to visualize how many components are necessary
fviz_eig(goat_loess_acp, addlabels = TRUE) #to visualize how many components are necessary
fviz_eig(goat_tpm_acp, addlabels = TRUE) #to visualize how many components are necessary
sessionInfo()## R version 4.0.4 (2021-02-15)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.4 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=fr_FR.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=fr_FR.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=fr_FR.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=fr_FR.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] Hmisc_4.5-0 Formula_1.2-4 survival_3.2-11 lattice_0.20-44
## [5] reshape2_1.4.4 factoextra_1.0.7 ggplot2_3.3.5 FactoMineR_2.4
## [9] corrplot_0.90
##
## loaded via a namespace (and not attached):
## [1] sass_0.4.0 tidyr_1.1.3 jsonlite_1.7.2
## [4] splines_4.0.4 carData_3.0-4 bslib_0.2.5.1
## [7] assertthat_0.2.1 highr_0.9 latticeExtra_0.6-29
## [10] cellranger_1.1.0 yaml_2.2.1 ggrepel_0.9.1
## [13] pillar_1.6.1 backports_1.2.1 glue_1.4.2
## [16] digest_0.6.27 RColorBrewer_1.1-2 ggsignif_0.6.2
## [19] checkmate_2.0.0 colorspace_2.0-2 htmltools_0.5.1.1
## [22] Matrix_1.3-3 plyr_1.8.6 pkgconfig_2.0.3
## [25] broom_0.7.8 haven_2.4.1 purrr_0.3.4
## [28] scales_1.1.1 openxlsx_4.2.4 jpeg_0.1-8.1
## [31] rio_0.5.27 htmlTable_2.2.1 tibble_3.1.2
## [34] car_3.0-11 generics_0.1.0 farver_2.1.0
## [37] ellipsis_0.3.2 ggpubr_0.4.0 DT_0.18
## [40] withr_2.4.2 nnet_7.3-16 readxl_1.3.1
## [43] magrittr_2.0.1 crayon_1.4.1 evaluate_0.14
## [46] fansi_0.5.0 MASS_7.3-54 forcats_0.5.1
## [49] rstatix_0.7.0 foreign_0.8-81 tools_4.0.4
## [52] data.table_1.14.0 hms_1.1.0 lifecycle_1.0.0
## [55] stringr_1.4.0 munsell_0.5.0 zip_2.2.0
## [58] cluster_2.1.2 flashClust_1.01-2 compiler_4.0.4
## [61] jquerylib_0.1.4 rlang_0.4.11 grid_4.0.4
## [64] rstudioapi_0.13 htmlwidgets_1.5.3 leaps_3.1
## [67] base64enc_0.1-3 labeling_0.4.2 rmarkdown_2.14
## [70] gtable_0.3.0 abind_1.4-5 curl_4.3.2
## [73] DBI_1.1.1 R6_2.5.0 gridExtra_2.3
## [76] knitr_1.33 dplyr_1.0.7 utf8_1.2.1
## [79] stringi_1.6.2 Rcpp_1.0.7 vctrs_0.3.8
## [82] rpart_4.1-15 png_0.1-7 scatterplot3d_0.3-41
## [85] tidyselect_1.1.1 xfun_0.31